AI Software Engineer

About

We are seeking a hands-on AI Software Engineer to design, build, and deploy intelligent backend systems that power conversational AI, automation, and data-driven decision engines. You’ll collaborate with data scientists, ML engineers, and product teams to integrate LLM-based models (OpenAI, Anthropic, Meta Llama, etc.) into scalable microservices and internal tools.

Key Responsibilities

• Design and develop Python-based backend systems supporting AI/LLM workflows, APIs, and data pipelines.
• Build scalable microservices and vector-database integrations (e.g., Milvus, Pinecone, FAISS) for retrieval-augmented generation (RAG) pipelines.
• Integrate and orchestrate LLMs using APIs (OpenAI, Anthropic, Hugging Face, vLLM, Triton, or similar).
• Work closely with data engineering to optimize data ingestion, preprocessing, and embeddings pipelines.
• Implement asynchronous and distributed processing (Celery, Kafka, or Ray).
• Deploy and monitor services on Docker/Kubernetes with CI/CD pipelines (GitHub Actions, Jenkins, or GitLab CI).
• Maintain documentation, testing, and model performance metrics.
• Collaborate with DevOps and security to ensure safe and reliable AI deployments.

Required Skills & Experience

• 3+ years experience in backend or full-stack development with Python (FastAPI, Flask, or Django).
• Proven experience integrating AI/ML or NLP systems (LLMs, embeddings, transformers, etc.).
• Strong understanding of RESTful and async APIs, data serialization, and model inference optimization.
• Familiarity with vector databases (Milvus, Pinecone, FAISS, Weaviate) and document chunking/embedding techniques.
• Experience with SQL and NoSQL databases (PostgreSQL, MongoDB, Redis).
• Hands-on with Docker, Kubernetes, and cloud environments (AWS / GCP / Azure).
• Knowledge of MLOps workflows (model packaging, inference serving, versioning).
• Experience with Git, CI/CD, and automated testing.

Nice to Have

• Familiarity with AI voice technologies (Riva, ElevenLabs, VAPI SDK, or similar).
• Experience with LangChain, LlamaIndex, or Haystack for RAG pipelines.
• Exposure to NVIDIA Triton / TensorRT-LLM / vLLM for high-performance inference.
• Understanding of prompt engineering, retrieval evaluation, and fine-tuning pipelines.
• Experience contributing to open-source AI frameworks.

Why Join Us

• Build real AI products — from voice agents to LLM-powered automation systems — not just prototypes.
• Work with a high-performance engineering team using NVIDIA hardware and cutting-edge open-source tools.
• 100% remote flexibility, cross-functional collaboration, and ownership of critical AI systems.